In this article, we first tackle a more realistic domain adaptation (DA) setting: source-free blending-target DA (SF-BTDA), where we cannot access to source-domain data while facing mixed multiple target domains without any domain labels in prior. Compared to existing DA scenarios, SF-BTDA generally faces the coexistence of different label shifts in different targets, along with noisy target pseudolabels generated from the source model. In this article, we propose a new method called evidential graph contrastive alignment (EGCA) to decouple the blending-target domain and alleviate the effect of noisy target pseudolabels. First, to improve the quality of pseudo target labels, we propose a calibrated evidential learning (CEL) module to iteratively improve both the accuracy and certainty of the resulting model and adaptively generate high-quality pseudo target labels. Second, we design a graph contrastive learning with the domain distance matrix and confidence-uncertainty criterion, to minimize the distribution gap of samples of the same class in the blending-target domain, which alleviates the coexistence of different label shifts in blended targets. We conduct a new benchmark based on three standard DA datasets, and EGCA outperforms other methods with considerable gains and achieves comparable results compared with those that have domain labels or source data in prior.

Seasonal-scale climate prediction plays a critical role in supporting agricultural planning, disaster prevention, and long-term decision making. In particular, reliable forecasts issued 1-6 months in advance are essential for early warning of flood and drought risks associated with precipitation during the East Asian summer monsoon season. However, while the use of machine learning techniques has advanced rapidly in weather and subseasonal-to-seasonal forecasting, partly driven by the availability of benchmark datasets, their application to seasonal-scale prediction remains limited. Existing seasonal prediction primarily relies on ensemble forecasts from numerical models, which, while physically grounded, are subject to biases and uncertainties at long lead times. Motivated by these challenges, we propose SeasonBench-EA, a benchmark dataset for seasonal prediction in East Asia region. It features multi-resolution, multi-source data with both regional and global coverage, integrating ERA5 reanalysis data and ensemble forecasts from multiple leading forecast centers. Beyond key atmospheric fields, the dataset also includes boundary-related variables, such as ocean state, soil and solar radiation, that are essential for capturing seasonal-scale atmospheric variability. Two tasks are defined and evaluated: 1) machine learning-based seasonal prediction using ERA5 reanalysis, and 2) post-processing of seasonal forecasts from numerical model ensembles. A suite of deterministic and probabilistic metrics is provided for tasks evaluation, along with a hindcast assessment focused on precipitation during the East Asian summer monsoon, aligned with model evaluation protocols used in operations. By offering a unified data and evaluation framework, SeasonBench-EA aims to promote the development and application of data-driven methods for seasonal prediction, a challenging yet highly impactful task with board implications for society and public well-being. Our benchmark is available at https://github.com/SauryChen/SeasonBench-EA.

Reference-based super-resolution (RefSR) has the potential to build bridges across spatial and temporal resolutions of remote sensing images. However, existing RefSR methods are limited by the faithfulness of content reconstruction and the effectiveness of texture transfer in large scaling factors. Conditional diffusion models have opened up new opportunities for generating realistic high-resolution images, but effectively utilizing reference images within these models remains an area for further exploration. Furthermore, content fidelity is difficult to guarantee in areas without relevant reference information. To solve these issues, we propose a change-aware diffusion model named Ref-Diff for RefSR, using the land cover change priors to guide the denoising process explicitly. Specifically, we inject the priors into the denoising model to improve the utilization of reference information in unchanged areas and regulate the reconstruction of semantically relevant content in changed areas. With this powerful guidance, we decouple the semantics-guided denoising and reference texture-guided denoising processes to improve the model performance. Extensive experiments demonstrate the superior effectiveness and robustness of the proposed method compared with state-of-the-art RefSR methods in both quantitative and qualitative evaluations. The code and data are available at https://github.com/dongrunmin/RefDiff.

With the increasing spatial and temporal resolutions of obtained remote sensing (RS) images, effective compression becomes critical for storage, transmission, and large-scale in-memory processing. Although image compression methods achieve a series of breakthroughs for daily images, a straightforward application of these methods to RS domain underutilizes the properties of the RS images, such as content duplication, homogeneity, and temporal redundancy. This paper proposes a Spatial-Temporal Context model (STCM) for RS image compression, jointly leveraging context from a broader spatial scope and across different temporal images. Specifically, we propose a stacked diagonal masked module to expand the contextual reference scope, which is stackable and maintains its parallel capability. Furthermore, we propose spatial-temporal contextual adaptive coding to enable the entropy estimation to reference context across different temporal RS images at the same geographic location. Experiments show that our method outperforms previous state-of-the-art compression methods on rate-distortion (RD) performance. For downstream tasks validation, our method reduces the bitrate by 52 times for single temporal images in the scene classification task while maintaining accuracy.

Fine urban change segmentation using multi-temporal remote sensing images is essential for understanding human-environment interactions in urban areas. Although there have been advances in high-quality land cover datasets that reveal the physical features of urban landscapes, the lack of fine-grained land use datasets hinders a deeper understanding of how human activities are distributed across landscapes and the impact of these activities on the environment, thus constraining proper technique development. To address this, we introduce FUSU, the first fine-grained land use change segmentation dataset for Fine-grained Urban Semantic Understanding. FUSU features the most detailed land use classification system to date, with 17 classes and 30 billion pixels of annotations. It includes bi-temporal high-resolution satellite images with 0.2-0.5 m ground sample distance and monthly optical and radar satellite time series, covering 847 km^2 across five urban areas in the southern and northern of China with different geographical features. The fine-grained land use pixel-wise annotations and high spatial-temporal resolution data provide a robust foundation for developing proper deep learning models to provide contextual insights on human activities and urbanization. To fully leverage FUSU, we propose a unified time-series architecture for both change detection and segmentation. We benchmark FUSU on various methods for several tasks. Dataset and code are available at: https://github.com/yuanshuai0914/FUSU.

Various remote sensing applications have widely used domain adaptation (DA) methods. Since it does not need to add human interpretation in the target domain, it can be used in cross-region, multi-temporal, and multi-sensor application scenarios. In order to further optimize the design of the loss function and better address the challenges of DA in remote sensing, in this paper, we propose a new universal DA method named C3DA for scene recognition of remote sensing images. It has a comprehensive C3criterion for recognizing the "unknown" classes by innovatively fusing confidence, consistency, and certainty of samples to make our network training more efficient. We evaluate the performance of our proposed method based on six transfer tasks on three remote sensing datasets. The evaluation results show that our proposed method achieves an average H-score of 58.44%, significantly higher than other SOTA universal DA methods with an average improvement of 2.32~29.43%. Compared to the baseline ResNet-50, it achieves up to 19.92% improvement, demonstrating that the proposed method outperforms in the universal DA scenario. In the future, we also plan to expand the application of this method to more scenarios.

Land Cover Mapping (LCM) is an important task to detect and understand the change of the earth surface. However, most LCM methods adopt supervised classifiers, and suffer from a lack of labels at a large scale. In this paper, we propose Fast-LCM, a scalable and weakly-supervised LCM method on a new Sunway supercomputer to achieve large-scale land cover mapping, requiring no manual annotations. Fast-LCM includes two major parts: (1) a distance-guided k-means module that combines textural, spectral, and temporal features, and (2) an automatic voting-based merging strategy to give each cluster a real meaning of classification system. Through careful parallelization, our Fast-LCM method scales to over 38 million cores, and provides a sustained performance for the task of China LCM. We produce a 10m resolution land cover map of China within only 3 minutes, including 1.2 minutes for IO and only 55 seconds to finish the computation. Fast-LCM achieves an accuracy of 68.85% (25-class), with 3.64% to 7.05% higher than best existing products.

High-resolution land cover mapping (LCM) is an important application for studying and understanding the change of the earth surface. While deep learning (DL) methods demonstrate great potential in analyzing satellite images, they largely depend on massive high-quality labels. This paper proposes SW-LCM, a Scalable and Weakly-supervised two-stage Land Cover Mapping method on a new Sunway Supercomputer. Our method consists of a k-means clustering module as a first stage, and an iterative deep learning module as a second stage. With the k-means module providing a good enough starting point (taking inaccurate results as noisy labels), the deep learning module improves the classification results in an iterative way, without any labelling efforts required for processing large scenarios. To achieve efficiency for country-level land cover mapping, we design a customized data partition scheme and an on-the-fly assembly for k-means. Through careful parallelization and optimization, our k-means module scales to 98,304 computing nodes (over 38 million cores), and provides a sustained performance of 437.56 PFLOPS, in a real LCM task of the entire region of China; the iterative updating part scales to 24,576 nodes, with a performance of 11 PFLOPS. We produce a 10-m resolution land cover map of China, with an accuracy of 83.5% (10-class) or 73.2% (25-class), 7% to 8% higher than best existing products, paving ways for finer land surveys to support sustainability-related applications.

Molecular docking is the process of posing, scoring, and ranking small molecules at the binding sites of proteins to prioritize compounds for experimental testing. It is a widely-used computational method in the drug discovery process. However, it is a highly time-consuming procedure since a receptor may need to find favorable ligand orientations in billions of ligands. UCSF DOCK3.7 is one of the most widely used molecular docking applications. In this paper, we port and optimize UCSF DOCK3.7 on the Sunway TaihuLight supercomputer. To avoid the impact of load imbalance, we employ a producer-consumer strategy that can overlap I/O and computation in order to achieve high performance. Furthermore, we present a new binary file format to replace the mol2db2 file format for ligand storage and adopt xzip rather than gzip to compress ligand files. We show that our file format can reduce I/O time significantly while xzip saves significant storage. For the routines which determine the orientation of a ligand relative to the receptor, we present an improved algorithm to discard geometrically similar orientations. Furthermore, we fuse loops and compress memory usage to store data in fast Local Device Memory (LDM) in order to score ligand orientations with high efficiency. In addition, we propose a number of architecture-specific optimizations. Asynchronous data transfer and vectorization of computation are implemented to take full advantage of the SW26010 processor. Our experiments show that a speedup of 167 can be achieved by using the proposed strategies. Compared to a core of an Intel(R) Core(TM) i9-10900K CPU, our approach achieves speedups of 15 on a SW26010 core group. Furthermore, our implementation achieves strong scalability to hundreds of thousands of heterogeneous cores on the next-generation Sunway supercomputer.

MOTIVATION: Mash is a popular hash-based genome analysis toolkit with applications to important downstream analyses tasks such as clustering and assembly. However, Mash is currently not able to fully exploit the capabilities of modern multi-core architectures, which in turn leads to high runtimes for large-scale genomic datasets. RESULTS: We present RabbitMash, an efficient highly optimized implementation of Mash which can take full advantage of modern hardware including multi-threading, vectorization and fast I/O. We show that our approach achieves speedups of at least 1.3, 9.8, 8.5 and 4.4 compared to Mash for the operations sketch, dist, triangle and screen, respectively. Furthermore, RabbitMash is able to compute the all-versus-all distances of 100 321 genomes in <5 min on a 40-core workstation while Mash requires over 40 min. AVAILABILITY AND IMPLEMENTATION: RabbitMash is available at https://github.com/ZekunYin/RabbitMash. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GROMACS is one of the most popular Molecular Dynamic (MD) applications and is widely used in the field of chemical and bimolecular system study. Similar to other MD applications, it needs long run-time for large-scale simulations. Therefore, many high performance platforms have been employed to accelerate it, such as Knights Landing (KNL), Cell Processor, Graphics Processing Unit (GPU) and so on. As the third fastest supercomputer in the world, Sunway TaihuLight contains 40960 SW26010 processors and SW26010 is a typical many-core processor. To make full use of the superior computation ability of TaihuLight, we port GROMACS to SW26010 with following new strategies: (1) a new deferred update strategy; (2) a new update mark strategy; (3) a full pipeline acceleration. Furthermore, we redesign GROMACS to enable all possible vectorization. Experiments show that our implementation achieves better performance than both Intel KNL and Nvidia P100 GPU when using appropriate number of SW26010 processors for a fair comparison.